In the oil and gas industry, for example, the efficiency of execution of a potential industrial plant (e.g. oil well) is extremely important as it has a significant impact on the capital cost of the operation. For example, the capital costs may be increased by technical difficulties relating to drilling. More specifically, if an oil well is drilled in the wrong location, the drilling phase may be lengthened or made more difficult (e.g. through geological factors such as rock type and oil depth) and/or the yield may be less than optimal.
Capital expenditures is particularly imported in engineering in the Upstream Energy sector, and so facilitating efficient decision making at a fundamental level has the potential to generate a large following within the industry.
To give an indication of the scale of the issue in monetary terms, Canadian Oil and Gas Exploration & Production revenue for 2015 was originally predicted to be $104.2 billion dollars. Of this, $45.9 billion was forecasted capital expenditures, with 6982 wells being drilled. Estimating 1% of the capital expenditures for well construction engineering results in $458 million in capital spent on well construction engineering.
Given the large investment required, it is important that location and historical information is available in an accessible format at the planning stage. This is particularly the case for industrial plants whose location is limited by natural geographic features (e.g. mining and oil and gas wells).
In the past, geographical and historical data for previous industrial plants in the oil and gas sector have been stored in a conventional database. For example, Current GIS systems in the marketplace (IHS Accumap, GeoLogic GeoScout, Canadian Discovery Frac Database) only take publicly available information and digitize it. Due to the large quantity of data (corresponding to hundreds of thousands of industrial plants, each plant's performance being measured by a variety of performance metrics), this information has not been available in a sufficiently accessible way because of the processing power required to filter and process the data to allow a user to interact with it.
For example, CNBC published an online news article entitled “Oil firms are swimming in data they don't use” (author: Tom DiChristopher; date: 5 Mar. 2015). The article reported that a study by McKinsey & Company found that the less than 1 percent of the information gathered by the oil industry was being made available to the people in the industry who make decisions. The article suggested that, as a result, drillers are almost certainly operating below peak performance. The article also highlighted a report by consulting firm Bain & Co which estimated that better data analysis could help oil and gas companies boost production by 6 to 8 percent. The article concludes by stating that a problem is that while oilfield sensors offer real-time data on operations, the information is usually used to make immediate, binary decisions rather than being stored, filtered and analysed to inform future decision making.